Workshops

LATIS offers a series of workshops that are free and open to all faculty and graduate students. Join our LATIS Research Workshops Google Group to be the first to learn about workshops. You can view the slides and materials from past workshops at the LATIS Workshop Materials website.

Fall 2020 Workshops

All workshops held on Fridays from 10am-noon via Zoom

 

Online Data Collection | September 25th | Register for this workshop

Reproducible Experimental Design in Qualtrics | October 2nd | Register for this workshop

Introduction to R | October 9th | Register for this workshop

Introduction to Python for Social Science | October 16th | Register for this workshop

Introduction to NVivo | October 23rd | Register for this workshop

Introduction to Web APIs in Python | October 30th | Register for this workshop

Introduction to Web Scraping | November 6th | Register for this workshop

Remote Computing Resources in CLA | November 13th | Register for this workshop

 

 

Workshop descriptions

 

Online Data Collection - Tools & Things to Look Out For

This 1-hour webinar (plus Q&A session) is designed to help you explore different ways to go about collecting your data online. We will give an overview of things to consider when selecting a data collection platform, and compare and contrast different tools. We will focus on two aspects of online data collection: 

Instrument Creation, including considerations and capabilities for precision timing, customizability, technical knowledge, accessibility, interoperability, reproducibility, security, and more. We will compare several coding and non-coding based tools. 

Participant Recruitment, including task needs, sampling needs, naivety of participants, and data quality. We will compare several common online recruitment platforms. 

 

Reproducible Experimental Design in Qualtrics

Qualtrics is a versatile data collection tool that is available to all University researchers, and it can be used for a wide range of survey and experimental needs. However, finding the right bells and whistles when using this tool for your research can be daunting. This workshop will teach you how to develop online experiments using this tool and introduce best practices for reproducibility & data management so that your future self loves what you did.

Workshop format:

  • BEFORE WORKSHOP: Asynchronous materials to review on Canvas (35-45 minutes)
  • DURING WORKSHOP:
    • Open help session/time to review materials on Zoom
    • Live Demonstration on Qualtrics & Activities 
    • Open help session/time to work on activities on Zoom

This workshop will cover how to:

  • Randomize participants to conditions, using simple methods and more complex survey logic
  • How to customize participant survey/task paths based on responses
  • Embed multimedia stimuli into Qualtrics instruments
  • Use embedded data and piping to further customize your instrument
  • Create reproducible question flows with Qualtrics’ “Loop & Merge” tool
  • Capture important survey metadata so that others can reproduce your survey
  • Edit metadata information (i.e., recode values, variable names, question labels, etc.) within Qualtrics to maximize data management efficiency 
  • Integrate Qualtrics with other online tools, such as Amazon’s Mechanical Turk, or other surveys

To be successful, you should have:


Introduction to R

R is a popular tool for data analysis and statistical computing, and is a great alternative to tools like SPSS, Stata, or Excel. R is free and designed for reproducible research. This workshop will teach you how to get started using R to explore and clean your data. We will focus on issues social scientists often encounter when using data in R. This workshop will contain asynchronous and synchronous components.  

Workshop format:

  • BEFORE WORKSHOP: Asynchronous materials to review on Canvas (2 sections: 30-40 minutes each)
  • DURING WORKSHOP:
    • Open help session/time to review materials on Zoom
    • Live Demonstration on Zoom
    • Open help session/time to work on activities on Zoom

This workshop will cover how to:

  • Create an R script (syntax/command file) to capture data cleaning steps in a reproducible way
  • Load a comma-delimited spreadsheet (.csv) into R as a dataset
  • View and examine data in R 
  • Check and correct missing values, rename variables, create new variables, and recode values in the data 
  • Save cleaned data file in formats for later use in R or other applications

To be successful, you should have:

  • A familiarity with data used in the social sciences
  • A familiarity with another statistical or data processing tool, such as SPSS, Stata, SAS, or Excel

 

Introduction to Python for Social Science

Python has seen wide adoption in academic research because it is a powerful but easy-to-learn programming language. It can be used in a manner similar to R or Stata for statistical processing, but also provides wider application in data processing, collection, and file management. Python is free and can be used in many phases of a project to enhance the reproducibility of research. This workshop will teach you how to get started using Python and some of its basic syntax, grammar and structures. It will also introduce the popular package Pandas which provides a familiar dataframe structure to import, format, and clean data as well as functions to manipulate, filter, and analyze data.

This workshop will cover how to:

  • Use Python 3 in a JupyterLab computing environment
  • Create an script (syntax/command file) to capture steps in a reproducible way
  • Use Python to grab data from a large number of files quickly
  • Load a comma-delimited spreadsheet (.csv) into Pandas as a dataframe
  • View and clean that data
  • Save cleaned data file in formats for later use

To be successful, you should have:

  • A familiarity with data used in the social sciences
  • A familiarity with another statistical or data processing tool, such as R, SPSS, Stata, SAS, or Excel
  • A laptop you can bring to the workshop

 

Introduction to NVivo

NVivo is a qualitative data management, coding and markup tool, that facilitates powerful querying and exploration of source materials for both mixed methods and qualitative analysis. It integrates well with tools that assist in data collection and can handle a wide variety of source materials. This workshop introduces the basic functions of NVivo, with no prior experience necessary. Licensing is provided for faculty and graduate students of the College of Liberal Arts; others can run the software in trial-mode for two weeks or can be given temporary access to the software for this workshop. 

This workshop will cover

  • Adding your source materials (text, images, audio/video, survey/spreadsheets)
  • Working with concepts (or codes/tags) and their definitions
  • Making annotations and analytical memos
  • Using text queries to speed up coding
  • Finding patterns in the concepts identified in the source materials
  • Importing data from other tools including Qualtrics, OneNote, and Zotero
  • Exporting excerpts and making backups
  • Working in teams

To be successful, you should

  • Be familiar with source materials used in qualitative research (interviews, focus groups, field notes, archival documents, etc.)
  • Be familiar with the types of questions asked in qualitative research
  • Download and install NVivo from z.umn.edu/getNVivo prior to the workshop

 

Introduction to Web APIs in Python

Web APIs (Application Programming Interfaces) provide a way for scholars to efficiently and legally access and download data from web platforms and publications such as Twitter and the New York Times. In this workshop we’ll use Python to query and download data using the NY Times API.

This workshop will cover how to:

  • Use Python 3 in a JupyterLab computing environment
  • Read API documentation to build successful API queries
  • Use the Requests and JSON Python libraries to download data from the NY Times API
  • Use built-in Python functions such as type, len, and dir to explore API data
  • Explore API data in Python using dictionaries

To be successful, you should have:

  • A computer you can use during the workshop, with
  • No prior experience with these tools is necessary, and participants do not need to have any coding skills. 
  • The Introduction to Python workshop on October 16, 2020 is not required, but recommended.

 

Introduction to Web Scraping

The internet is full of information waiting for exploration - from social media, to newspaper comments, to digitized archives. How do you begin gathering this kind of data? This workshop will introduce participants to browser-based tools for web scraping as well as reproducible web scraping methods using Python. We will cover essential legal literacies to ensure you can make informed decisions about when and how to web scrape following legal and ethical best practices.

This workshop will cover how to:

  • View and explore the HTML tree underlying every webpage you see
  • Use a browser extension (Scraper) to systematically copy sections of matching HTML from a single webpage
  • Use Python 3 in a JupyterLab computing environment
  • Use the Requests and BeautifulSoup Python libraries to access HTML data from the web
  • Create variables, lists and loops to work with web data in Python
  • Store and view HTML data in Pandas dataframe format

To be successful, you should have:

  • A computer you can use during the workshop, with the following installed:
  • No prior experience with these tools is necessary, and participants do not need to have any coding skills. 
  • The Introduction to Python workshop on October 16, 2020 is not required, but recommended.

 

Remote Computing Resources in CLA

This workshop will introduce the remote computing resources available to CLA graduate students and faculty.  It will provide an overview of the main resources available, including the remote windows computing (Windows Terminal Server; WTS) and the high performance linux system, compute.cla. Participants will learn how to use these systems,  including relevant data storage and secure computing practices.  

The course will consist of 3 sections:

  1. BEFORE WORKSHOP: Asynchronous introduction to the systems on Canvas
  2. DURING WORKSHOP:
    1. 10:00 - Open help session/time to review materials and install tools on Zoom
    2. 10:30 - Live Demonstration on Zoom of how to connect and use WTS and compute.cla
    3. Data storage and security considerations
  3. 11:30 - Live Q & A - for beginner through intermediate users in one-on-one meetings with LATIS staff.

To be successful, you should: